Duygu ve his analizi

sentiments veri kümesi içinde, nrc sözlüğü belirli kelimeleri o kelimeyle ilişkili bir duyguyla eşleyen bir sözlük içerir. Bu veri kümesinde joy (neşe), trust (güven), anticipation (beklenti) gibi duygular bulunur.

İncelediğin Rus tweet botu veri kümesinde, hem sola hem de sağa eğilimli tweet botlarının attığı tweet’lere baktın. nrc sözlüğünü kullanarak, sola eğilimli (demokrat) tweet botunun gönderdiği tweet’lerin içeriğini keşfet. Sol tweet’ler left içinde ve durak kelimeleri çıkarılarak sözcüklerine ayrılmış durumda.

Bu egzersiz

R ile Doğal Dil İşlemeye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

nrc sözlüğünden yalnızca anticipation (beklenti) kelimelerini içeren bir tibble oluştur.
nrc sözlüğünden yalnızca joy (neşe) kelimelerini içeren bir tibble oluştur.
left_tokens içinde bulunan en sık anticipation kelimelerini yazdır.
left_tokens içinde bulunan en sık joy kelimelerini yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

left_tokens <- left %>%
  unnest_tokens(output = "word", token = "words", input = content) %>%
  anti_join(stop_words)
# Dictionaries 
anticipation <- ___("nrc") %>% 
  ___(sentiment == "anticipation")
joy <- ___("nrc") %>% 
  ___(sentiment == "joy")
# Print top words for Anticipation and Joy
left_tokens %>%
  ___(anticipation, by = "word") %>%
  ___(word, sort = TRUE)
left_tokens %>%
  ___(joy, by = "word") %>%
  ___(word, sort = TRUE)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Doğal Dil İşlemeye Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Chapter 1 of Introduction to Natural Langauge Processing prepares you for running your first analysis on text. You will explore regular expressions and tokenization, two of the most common components of most analysis tasks. With regular expressions, you can search for any pattern you can think of, and with tokenization, you can prepare and clean text for more sophisticated analysis. This chapter is necessary for tackling the techniques we will learn in the remaining chapters of this course.

Exercise 1: Regular expression basics Exercise 2: Practicing syntax with grep Exercise 3: Exploring regular expression functions.Exercise 4: Tokenization Exercise 5: tidytext functions Exercise 6: Tokenization: sentences Exercise 7: Text cleaning basics Exercise 8: Text preprocessing: remove stop words Exercise 9: Text preprocessing: Stemming

In this chapter, you will learn the most common and studied ways to analyze text. You will look at creating a text corpus, expanding a bag-of-words representation into a TFIDF matrix, and use cosine-similarity metrics to determine how similar two pieces of text are to each other. You build on your foundations for practicing NLP before you dive into applications of NLP in chapters 3 and 4.

Exercise 1: Understanding an R corpus Exercise 2: Explore an R corpus Exercise 3: Creating a tibble from a corpus Exercise 4: Creating a corpus Exercise 5: The bag-of-words representation Exercise 6: Practice BoW Exercise 7: BoW Example Exercise 8: Sparse matrices Exercise 9: The TFIDF Exercise 10: Manual calculations Exercise 11: TFIDF Practice Exercise 12: Cosine Similarity Exercise 13: An example of failing at text analysis Exercise 14: Cosine similarity example

Chapter 3 focuses on two common text analysis approaches, classification modeling, and topic modeling. If you are working on text analysis projects, you will inevitably use one or both of these methods. This chapter teaches you how to perform both techniques and provides insight into how to approach these techniques from a practical point of you.

Exercise 1: Preparing text for modeling Exercise 2: Data preparation Exercise 3: Removing sparse terms Exercise 4: Classification modeling Exercise 5: Classification modeling example Exercise 6: Confusion matrices Exercise 7: TFIDF tibble vs dtm Exercise 8: Introduction to topic modeling Exercise 9: LDA practice Exercise 10: Assigning topics to documents Exercise 11: LDA in practice Exercise 12: Testing perplexity Exercise 13: Reviewing LDA results

In chapter 4 we cover two staples of natural language processing, sentiment analysis, and word embeddings. These are two analysis techniques that are a must for anyone learning the fundamentals of text analysis. Furthermore, you will briefly learn about BERT, part-of-speech tagging, and named entity recognition. Almost 15 different analysis techniques were covered in this course, so chapter 4 ends by recapping all of the great techniques you will learn about in this course.

Exercise 1: Duygu analizi Exercise 2: tidytext sözlükleri Exercise 3: Duygu puanları Exercise 4: Duygu ve his analizi

Geçerli Egzersiz

Exercise 5: Kelime gömme (word embeddings)Exercise 6: h2o uygulaması Exercise 7: word2vec Exercise 8: Ek NLP analizleri Exercise 9: Yöntemleri gözden geçirme #1 Exercise 10: Yöntemleri gözden geçir #2 Exercise 11: Sonuç